{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from train_utils import Train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from model import SRResnet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "architecture = SRResnet(3, 64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = {\n",
    "    'noise_model': ('text', 0.3),\n",
    "    'crop_size': 64,\n",
    "    'clean_targs': False,\n",
    "    'lr': 0.001,\n",
    "    'epochs': 100,\n",
    "    'bs': 2,\n",
    "    'lossfn': 'l2',\n",
    "    'cuda': True\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainer = Train(architecture, 'dataset/train', 'dataset/valid', params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 10%|▉         | 14/146 [00:37<05:56,  2.70s/it]"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-6-3435b262f1ae>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/mnt/c/Users/shiva/Desktop/Noise2Noise-Pytorch/train_utils.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     41\u001b[0m                 \u001b[0msource\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_list\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     42\u001b[0m                 \u001b[0mtarget\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_list\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 43\u001b[0;31m                 \u001b[0m_op\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marchitecture\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mVariable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msource\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     44\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_list\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     45\u001b[0m                     \u001b[0m_loss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloss_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mVariable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_list\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0m_op\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mVariable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_list\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mVariable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtarget\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.pyenv/versions/3.6.0/envs/general/lib/python3.6/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    355\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    356\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 357\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    358\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    359\u001b[0m             \u001b[0mhook_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/mnt/c/Users/shiva/Desktop/Noise2Noise-Pytorch/model.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m     46\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     47\u001b[0m         \u001b[0m_op1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mact\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconv1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 48\u001b[0;31m         \u001b[0m_op2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconv2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_op1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     49\u001b[0m         \u001b[0mop\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconv3\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_op1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_op2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     50\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.pyenv/versions/3.6.0/envs/general/lib/python3.6/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    355\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    356\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 357\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    358\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    359\u001b[0m             \u001b[0mhook_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.pyenv/versions/3.6.0/envs/general/lib/python3.6/site-packages/torch/nn/modules/container.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m     65\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     66\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mmodule\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_modules\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 67\u001b[0;31m             \u001b[0minput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodule\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     68\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     69\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.pyenv/versions/3.6.0/envs/general/lib/python3.6/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    355\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    356\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 357\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    358\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    359\u001b[0m             \u001b[0mhook_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/mnt/c/Users/shiva/Desktop/Noise2Noise-Pytorch/model.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m     27\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     28\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 29\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mblock2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mblock1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     30\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     31\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.pyenv/versions/3.6.0/envs/general/lib/python3.6/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    355\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    356\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 357\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    358\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    359\u001b[0m             \u001b[0mhook_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/mnt/c/Users/shiva/Desktop/Noise2Noise-Pytorch/model.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m         \u001b[0mop\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     18\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mact\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mop\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muse_act\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.pyenv/versions/3.6.0/envs/general/lib/python3.6/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    355\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    356\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 357\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    358\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    359\u001b[0m             \u001b[0mhook_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.pyenv/versions/3.6.0/envs/general/lib/python3.6/site-packages/torch/nn/modules/conv.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m    280\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    281\u001b[0m         return F.conv2d(input, self.weight, self.bias, self.stride,\n\u001b[0;32m--> 282\u001b[0;31m                         self.padding, self.dilation, self.groups)\n\u001b[0m\u001b[1;32m    283\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    284\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.pyenv/versions/3.6.0/envs/general/lib/python3.6/site-packages/torch/nn/functional.py\u001b[0m in \u001b[0;36mconv2d\u001b[0;34m(input, weight, bias, stride, padding, dilation, groups)\u001b[0m\n\u001b[1;32m     88\u001b[0m                 \u001b[0m_pair\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgroups\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackends\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcudnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbenchmark\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     89\u001b[0m                 torch.backends.cudnn.deterministic, torch.backends.cudnn.enabled)\n\u001b[0;32m---> 90\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbias\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     91\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     92\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "trainer.train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "m = trainer.architecture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from data import NoisyDataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.utils.data import DataLoader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_ = NoisyDataset('dataset/valid', crop_size=128, clean_targ=True, train_noise_model=('text', 0.3)) # Default gaussian noise without clean targets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "dl = DataLoader(data_, batch_size=1, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import numpy as np\n",
    "from torch.autograd import Variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "def show(img, a):\n",
    "    plt.figure()\n",
    "    if a == 'v':\n",
    "        npimg = torch.squeeze(img).data.cpu().numpy()\n",
    "    else:\n",
    "        npimg = torch.squeeze(img).numpy()\n",
    "    plt.imshow(np.transpose(npimg, (1,2,0)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "i = 0\n",
    "for (source, target) in dl:\n",
    "    plt.figure()\n",
    "    pred = m(Variable(source.cuda()))\n",
    "    show(pred, 'v')\n",
    "    show(source, 't')\n",
    "    show(target, 't')\n",
    "    if i == 0:\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
